Bio-inspired computation is a rapidly evolving field with many real-world applications including business, management, and engineering. In addition, various techniques and algorithms have been developed over the last several decades within bio-inspired computation.

Solving an optimization problem by mathematical or traditional modeling methods can be difficult or even impossible in practice – due to, for example, the existence of non-convex functions and discrete variables. However, by applying bio-inspired methodologies, it is possible to solve complex optimization problems with high-quality solutions. As with problem-dependent improvement techniques, generating optimal solutions poses several challenges such as the design and analysis of bio-inspired algorithms. Recent studies have shown that the performance of some bio-inspired algorithms depends on the special characteristics of the optimization problems. However, finding an optimal solution for an optimization problem may not be easy.

This Special Issue intends to gather the latest high-quality original research and review articles discussing bio-inspired algorithms and their applications. We also welcome articles focusing on the analysis and design of optimization test problems, as well as performance evaluation indicators.

Potential topics include but are not limited to the following:

• Bio-inspired algorithms, such as genetic algorithms, evolutionary strategy, particle swarm optimization, ant colony optimization, and differential evolution
• Theoretical analyses of bio-inspired algorithms
• Thorough analysis and comparison of existing techniques and algorithms
• Bio-inspired algorithms for multi-objective optimization
• Hybrid bio-inspired algorithms
• Parallel and distributed bio-inspired algorithms
• Brain-inspired cognitive architectures
• Application of bio-inspired algorithms in other fields such as natural language processing, financial engineering, healthcare, time series analysis, recommender systems, and cybersecurity